[Attention] Update tests to remove deprecated env vars (#30563)

Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>

tests/v1/cudagraph/test_cudagraph_mode.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import contextlib
-import os
 import weakref
 from contextlib import ExitStack
 
@@ -13,26 +11,6 @@ from vllm import LLM
 from vllm.config import CompilationConfig, CompilationMode
 from vllm.platforms import current_platform
 
-
-@contextlib.contextmanager
-def temporary_environ(env_vars):
-    """
-    Temporarily set environment variables and restore them afterward.
-    We have to do this vs monkeypatch because monkeypatch doesn't work
-    with "module" scoped fixtures.
-    """
-    original_env = {k: os.environ.get(k) for k in env_vars}
-    try:
-        os.environ.update(env_vars)
-        yield
-    finally:
-        for k, v in original_env.items():
-            if v is None:
-                os.environ.pop(k, None)
-            else:
-                os.environ[k] = v
-
-
 # test attention backend and cudagraph_mode combo
 # (backend_name, cudagraph_mode, supported)
 if current_platform.is_rocm():
@@ -68,9 +46,9 @@ def test_backend_and_cudagraph_mode_combo(backend_name, cudagraph_mode, supporte
     ):
         pytest.skip("Only Hopper GPUs support FA3 and FlashMLA")
 
-    env_vars = backend_configs[backend_name].env_vars
+    attention_config = backend_config.attention_config
 
-    with temporary_environ(env_vars), ExitStack() as stack:
+    with ExitStack() as stack:
         if not supported:
             stack.enter_context(pytest.raises(Exception))
 
@@ -80,6 +58,7 @@ def test_backend_and_cudagraph_mode_combo(backend_name, cudagraph_mode, supporte
             trust_remote_code=True,
             gpu_memory_utilization=0.45,
             max_model_len=1024,
+            attention_config=attention_config,
             compilation_config=CompilationConfig(
                 mode=CompilationMode.VLLM_COMPILE, cudagraph_mode=cudagraph_mode
             ),
@@ -122,9 +101,10 @@ combo_cases_2 = [
 def test_cudagraph_compilation_combo(
     backend_name, cudagraph_mode, compilation_mode, supported
 ):
-    env_vars = backend_configs[backend_name].env_vars
+    backend_config = backend_configs[backend_name]
+    attention_config = backend_config.attention_config
 
-    with temporary_environ(env_vars), ExitStack() as stack:
+    with ExitStack() as stack:
         if not supported:
             stack.enter_context(pytest.raises(Exception))
 
@@ -134,6 +114,7 @@ def test_cudagraph_compilation_combo(
             trust_remote_code=True,
             gpu_memory_utilization=0.45,
             max_model_len=1024,
+            attention_config=attention_config,
             compilation_config=CompilationConfig(
                 mode=compilation_mode, cudagraph_mode=cudagraph_mode
             ),

tests/v1/determinism/test_batch_invariance.py

@@ -28,7 +28,7 @@ IS_DEVICE_CAPABILITY_BELOW_90 = is_device_capability_below_90()
     BACKENDS,
 )
 def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
-    backend, monkeypatch: pytest.MonkeyPatch
+    backend,
 ):
     """
     Ensures that the same request (the 'needle' prompt) yields identical output
@@ -54,7 +54,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
 
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
+    attention_config = {"backend": backend}
     # Allow overrides from environment (useful for CI tuning)
     # "facebook/opt-125m" is too small, doesn't reliably test determinism
     model = resolve_model_name(backend)
@@ -92,6 +92,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
             max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
             max_model_len=max_model_len,
+            attention_config=attention_config,
         )
 
         # Baseline generation for the needle prompt alone.
@@ -106,6 +107,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
             max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
             max_model_len=max_model_len,
+            attention_config=attention_config,
         )
 
         mismatches = 0
@@ -163,10 +165,8 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     BACKENDS,
 )
 def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
-    backend, monkeypatch: pytest.MonkeyPatch
+    backend,
 ):
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
-
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
     model_name = resolve_model_name(backend)
@@ -193,6 +193,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
         dtype="bfloat16",  # not everything is supported
         gpu_memory_utilization=0.9,
         enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
+        attention_config={"backend": backend},
     )
 
     # Use more realistic prompts for better token generation
@@ -381,12 +382,11 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     "backend",
     BACKENDS,
 )
-def test_simple_generation(backend, monkeypatch: pytest.MonkeyPatch):
+def test_simple_generation(backend):
     """
     Simple test that runs the model with a basic prompt and prints the output.
     Useful for quick smoke testing and debugging.
     """
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
     model = resolve_model_name(backend)
 
     llm = LLM(
@@ -398,6 +398,7 @@ def test_simple_generation(backend, monkeypatch: pytest.MonkeyPatch):
         dtype="bfloat16",
         enable_prefix_caching=False,
         enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
+        attention_config={"backend": backend},
     )
 
     prompt = "the capital of france is"
@@ -444,8 +445,6 @@ def test_logprobs_without_batch_invariance_should_fail(
     The test will PASS if we detect differences (proving batch invariance matters).
     The test will FAIL if everything matches (suggesting batch invariance isn't needed).
     """
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
-
     # CRITICAL: Disable batch invariance for this test
     monkeypatch.setenv("VLLM_BATCH_INVARIANT", "0")
     monkeypatch.setattr(batch_invariant, "VLLM_BATCH_INVARIANT", False)
@@ -465,6 +464,7 @@ def test_logprobs_without_batch_invariance_should_fail(
         max_model_len=8192,
         dtype="bfloat16",
         enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
+        attention_config={"backend": backend},
     )
 
     # build ragged prompts to change shapes significantly across BS=1 vs BS=N
@@ -649,7 +649,7 @@ def test_logprobs_without_batch_invariance_should_fail(
 @skip_unsupported
 @pytest.mark.parametrize("backend", ["FLASH_ATTN"])
 def test_decode_logprobs_match_prefill_logprobs(
-    backend, monkeypatch: pytest.MonkeyPatch
+    backend,
 ):
     """
     Test that verifies decode logprobs match prefill logprobs.
@@ -664,8 +664,6 @@ def test_decode_logprobs_match_prefill_logprobs(
     This ensures that the logprobs from decode are consistent with what
     we would get if we ran prefill on each prefix.
     """
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
-
     seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
     random.seed(seed)
     model_name = resolve_model_name(backend)
@@ -689,6 +687,7 @@ def test_decode_logprobs_match_prefill_logprobs(
         max_model_len=8192,
         dtype="bfloat16",
         enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
+        attention_config={"backend": backend},
     )
 
     # Use a few test prompts
@@ -920,6 +919,7 @@ def LLM_with_max_seqs(
     max_num_seqs: int,
     gpu_memory_utilization: float,
     max_model_len: int,
+    attention_config: dict | None = None,
 ) -> LLM:
     """
     Helper to construct an LLM with a specific max_num_seqs (batch-size limit)
@@ -934,6 +934,7 @@ def LLM_with_max_seqs(
         tensor_parallel_size=int(os.getenv("VLLM_TP_SIZE", "1")),
         enable_prefix_caching=False,
         enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
+        attention_config=attention_config,
         # Enable for MOE models
         # enable_expert_parallel=True,
     )

tests/v1/determinism/test_online_batch_invariance.py

@@ -136,11 +136,9 @@ def _compare_bs1_vs_bsn_single_process(
 @skip_unsupported
 @pytest.mark.parametrize("backend", BACKENDS)
 def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
-    backend: str, monkeypatch: pytest.MonkeyPatch
+    backend: str,
 ) -> None:
     random.seed(int(os.getenv("VLLM_TEST_SEED", "12345")))
-    # Override backend for this test (and the RemoteOpenAIServer child process).
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend)
     model_name = resolve_model_name(backend)
     prompts_all = [_random_prompt(10, 50) for _ in range(32)]
 
@@ -156,6 +154,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     server_args: list[str] = [
         "--max-model-len=8192",
         "--max-num-seqs=32",
+        f"--attention-backend={backend}",
     ]
     if tp_size:
         server_args += ["-tp", tp_size]

tests/v1/e2e/test_async_scheduling.py

@@ -142,16 +142,17 @@ def run_tests(
     """Test consistency of combos of async scheduling, preemption,
     uni/multiproc executor with spec decoding."""
 
-    with monkeypatch.context() as m:
-        # avoid precision errors
+    # Determine attention config based on platform
     if current_platform.is_rocm():
         if is_testing_with_spec_decoding:
             # Use TRITON_ATTN for spec decoding test for consistency
-                m.setenv("VLLM_ATTENTION_BACKEND", "TRITON_ATTN")
+            attention_config = {"backend": "TRITON_ATTN"}
         else:
-                m.setenv("VLLM_ATTENTION_BACKEND", "ROCM_AITER_FA")
+            attention_config = {"backend": "ROCM_AITER_FA"}
     else:
-            m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
+        attention_config = {"backend": "FLEX_ATTENTION"}
+
+    with monkeypatch.context() as m:
         # lock matmul precision to full FP32 (IEEE)
         m.setenv("VLLM_FLOAT32_MATMUL_PRECISION", "ieee")
         # m.setenv("VLLM_BATCH_INVARIANT", "1")
@@ -174,6 +175,7 @@ def run_tests(
                 spec_config,
                 test_prefill_chunking=test_prefill_chunking,
                 is_testing_with_spec_decoding=is_testing_with_spec_decoding,
+                attention_config=attention_config,
             )
             outputs.append(test_results)
 
@@ -262,6 +264,7 @@ def run_test(
     spec_config: dict[str, Any] | None,
     test_prefill_chunking: bool,
     is_testing_with_spec_decoding: bool = False,
+    attention_config: dict[str, Any] | None = None,
 ):
     spec_decoding = spec_config is not None
     cache_arg: dict[str, Any] = (
@@ -301,6 +304,7 @@ def run_test(
         dtype=dtype,
         speculative_config=spec_config,
         disable_log_stats=False,
+        attention_config=attention_config,
         **cache_arg,
     ) as vllm_model:
         results = []

tests/v1/e2e/test_cascade_attention.py

@@ -10,7 +10,7 @@ from ...utils import create_new_process_for_each_test
 
 @create_new_process_for_each_test()
 @pytest.mark.parametrize("attn_backend", ["FLASH_ATTN", "FLASHINFER"])
-def test_cascade_attention(example_system_message, monkeypatch, attn_backend):
+def test_cascade_attention(example_system_message, attn_backend):
     prompt = "\n<User>: Implement fibonacci sequence in Python.\n<Claude>:"
 
     if attn_backend == "FLASHINFER":
@@ -19,10 +19,9 @@ def test_cascade_attention(example_system_message, monkeypatch, attn_backend):
             "needs investigation. See issue #25679."
         )
 
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
-
-        llm = LLM(model="Qwen/Qwen2-1.5B-Instruct")
+    llm = LLM(
+        model="Qwen/Qwen2-1.5B-Instruct", attention_config={"backend": attn_backend}
+    )
     sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
 
     # No cascade attention.

tests/v1/e2e/test_spec_decode.py

@@ -438,19 +438,17 @@ def test_eagle_correctness(
     should be the same when using eagle speculative decoding.
     model_setup: (method, model_name, eagle_model_name, tp_size)
     """
-    with monkeypatch.context() as m:
+    # Determine attention config
+    # Scout requires default backend selection because vision encoder has
+    # head_dim 88 being incompatible with FLASH_ATTN and needs to fall back
+    # to Flex Attn
     if "Llama-4-Scout" in model_setup[1] and attn_backend == "FLASH_ATTN":
-            # Scout requires default backend selection
-            # because vision encoder has head_dim 88 being incompatible
-            #  with FLASH_ATTN and needs to fall back to Flex Attn
-
-            # pass if not ROCm
         if current_platform.is_rocm():
             # TODO: Enable Flex Attn for spec_decode on ROCm
             pytest.skip("Flex Attn for spec_decode not supported on ROCm currently")
+        attention_config = None  # Let it fall back to default
     else:
-            m.setenv("VLLM_MLA_DISABLE", "1")
-            m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+        attention_config = {"backend": attn_backend}
 
     if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
         pytest.skip(
@@ -458,6 +456,9 @@ def test_eagle_correctness(
             "multi-token eagle spec decode on current platform"
         )
 
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_MLA_DISABLE", "1")
+
         if attn_backend == "ROCM_AITER_FA" and current_platform.is_rocm():
             if "deepseek" in model_setup[1].lower():
                 pytest.skip("ROCM_AITER_FA for deepseek not supported on ROCm platform")
@@ -471,7 +472,10 @@ def test_eagle_correctness(
         max_num_batched_tokens = 128 if enable_chunked_prefill else max_model_len
 
         ref_llm = LLM(
-            model=model_name, max_model_len=max_model_len, tensor_parallel_size=tp_size
+            model=model_name,
+            max_model_len=max_model_len,
+            tensor_parallel_size=tp_size,
+            attention_config=attention_config,
         )
         ref_outputs = ref_llm.chat(test_prompts, sampling_config)
         del ref_llm
@@ -492,6 +496,7 @@ def test_eagle_correctness(
             max_num_batched_tokens=max_num_batched_tokens,
             enable_chunked_prefill=enable_chunked_prefill,
             model_impl=model_impl,
+            attention_config=attention_config,
         )
         spec_outputs = spec_llm.chat(test_prompts, sampling_config)
         matches = 0

tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh

@@ -3,21 +3,29 @@ set -xe
 
 # Parse command line arguments
 KV_BUFFER_DEVICE="cuda"  # Default to cuda
+ATTENTION_BACKEND=""  # Default to empty (use vllm default)
 while [[ $# -gt 0 ]]; do
   case $1 in
     --kv_buffer_device)
       KV_BUFFER_DEVICE="$2"
       shift 2
       ;;
+    --attention-backend)
+      ATTENTION_BACKEND="$2"
+      shift 2
+      ;;
     *)
       echo "Unknown option $1"
-      echo "Usage: $0 [--kv_buffer_device <cuda|cpu>]"
+      echo "Usage: $0 [--kv_buffer_device <cuda|cpu>] [--attention-backend <backend>]"
       exit 1
       ;;
   esac
 done
 
 echo "Running accuracy tests with kv_buffer_device=$KV_BUFFER_DEVICE"
+if [[ -n "$ATTENTION_BACKEND" ]]; then
+  echo "Using attention backend: $ATTENTION_BACKEND"
+fi
 
 DECODER_KV_LAYOUT=${DECODER_KV_LAYOUT:-"HND"} # Default to HND, optional NHD
 if [[ "$DECODER_KV_LAYOUT" == "NHD" ]]; then
@@ -148,6 +156,11 @@ run_tests_for_model() {
     --tensor-parallel-size $PREFILLER_TP_SIZE \
     --kv-transfer-config '$KV_CONFIG'"
 
+    # Add attention backend config if specified
+    if [[ -n "$ATTENTION_BACKEND" ]]; then
+      BASE_CMD="${BASE_CMD} --attention-backend=$ATTENTION_BACKEND"
+    fi
+
     if [ -n "$model_args" ]; then
     FULL_CMD="$BASE_CMD $model_args"
     else
@@ -189,6 +202,11 @@ run_tests_for_model() {
     --gpu-memory-utilization $GPU_MEMORY_UTILIZATION \
     --kv-transfer-config '$KV_CONFIG'"
 
+    # Add attention backend config if specified
+    if [[ -n "$ATTENTION_BACKEND" ]]; then
+      BASE_CMD="${BASE_CMD} --attention-backend=$ATTENTION_BACKEND"
+    fi
+
   # DP-EP attention mode
   if [[ -z "$DP_EP" ]]; then
     BASE_CMD="${BASE_CMD} --tensor-parallel-size $DECODER_TP_SIZE"

tests/v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh

@@ -15,14 +15,14 @@ configs=(
 
 run_tests() {
   local label=$1
-  local extra_env=$2
+  local extra_args=$2
 
   echo "=== Running tests (${label}) ==="
   for cfg in "${configs[@]}"; do
-    echo "-> Running with ${cfg} ${extra_env:+and ${extra_env}}"
+    echo "-> Running with ${cfg} ${extra_args:+and ${extra_args}}"
     # Use 'env' to safely set variables without eval
-    if ! env ${extra_env} ${cfg} bash "${SCRIPT}"; then
-      echo "❌ Test failed for config: ${cfg} ${extra_env:+(${extra_env})}"
+    if ! env ${cfg} bash "${SCRIPT}" ${extra_args}; then
+      echo "❌ Test failed for config: ${cfg} ${extra_args:+(${extra_args})}"
       exit 1
     fi
   done
@@ -34,8 +34,8 @@ run_tests "default backend" ""
 
 # Check if FLASHINFER is set (non-empty)
 if [[ -n "${FLASHINFER:-}" ]]; then
-  echo "FLASHINFER is set, rerunning with VLLM_ATTENTION_BACKEND=FLASHINFER"
-  run_tests "FLASHINFER backend" "VLLM_ATTENTION_BACKEND=FLASHINFER"
+  echo "FLASHINFER is set, rerunning with --attention-backend FLASHINFER"
+  run_tests "FLASHINFER backend" "--attention-backend FLASHINFER"
 else
   echo "FLASHINFER not set, skipping FLASHINFER runs."
 fi

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -1132,7 +1132,7 @@ def _run_abort_timeout_test(llm: LLM, timeout: int):
         "TRITON_ATTN",
     ],
 )
-def test_register_kv_caches(dist_init, attn_backend, monkeypatch):
+def test_register_kv_caches(dist_init, attn_backend):
     """
     Test that register_kv_caches() properly calls nixl_wrapper methods with
     correct data.
@@ -1144,9 +1144,7 @@ def test_register_kv_caches(dist_init, attn_backend, monkeypatch):
        block layout info
     """
 
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
-
-    vllm_config = create_vllm_config()
+    vllm_config = create_vllm_config(attention_backend=attn_backend)
 
     # Import the appropriate backend based on the parameter
     if attn_backend == "FLASH_ATTN":

tests/v1/kv_connector/unit/utils.py

@@ -11,6 +11,7 @@ import torch
 
 from vllm import SamplingParams
 from vllm.config import (
+    AttentionConfig,
     CacheConfig,
     DeviceConfig,
     KVTransferConfig,
@@ -94,6 +95,7 @@ def create_vllm_config(
     dtype: str = "float16",
     cache_dtype: str = "auto",
     hf_overrides: dict[str, Any] | None = None,
+    attention_backend: str | None = None,
 ) -> VllmConfig:
     """Initialize VllmConfig For Testing."""
     model_config = ModelConfig(
@@ -124,12 +126,14 @@ def create_vllm_config(
         enable_permute_local_kv=enable_permute_local_kv,
         kv_connector_extra_config=kv_connector_extra_config or {},
     )
+    attention_config = AttentionConfig(backend=attention_backend)
     return VllmConfig(
         scheduler_config=scheduler_config,
         model_config=model_config,
         cache_config=cache_config,
         kv_transfer_config=kv_transfer_config,
         device_config=DeviceConfig("cpu"),
+        attention_config=attention_config,
     )
 
 

tests/v1/kv_offload/test_cpu_offloading.py

@@ -13,7 +13,6 @@ from vllm import LLM, SamplingParams, TokensPrompt
 from vllm.config import KVEventsConfig, KVTransferConfig
 from vllm.distributed.kv_events import BlockStored, KVEventBatch
 from vllm.platforms import current_platform
-from vllm.utils.system_utils import set_env_var
 
 CPU_BLOCK_SIZES = [48]
 ATTN_BACKENDS = ["FLASH_ATTN"]
@@ -180,12 +179,12 @@ def test_cpu_offloading(cpu_block_size: int, attn_backend: str) -> None:
         topic="test",
     )
 
-    with set_env_var("VLLM_ATTENTION_BACKEND", attn_backend):
     llm = LLM(
         model="meta-llama/Llama-3.2-1B-Instruct",
         gpu_memory_utilization=0.5,
         kv_events_config=kv_events_config,
         kv_transfer_config=kv_transfer_config,
+        attention_config={"backend": attn_backend},
     )
 
     events_endpoint = events_endpoint.replace("*", "127.0.0.1")

tests/v1/spec_decode/test_eagle.py

@@ -15,6 +15,7 @@ from tests.v1.attention.utils import (
 )
 from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import (
+    AttentionConfig,
     CacheConfig,
     DeviceConfig,
     ModelConfig,
@@ -38,6 +39,7 @@ eagle3_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
 def _create_proposer(
     method: str,
     num_speculative_tokens: int,
+    attention_backend: str | None = None,
     speculative_token_tree: list[tuple[int, ...]] | None = None,
 ) -> EagleProposer:
     model_config = ModelConfig(model=model_dir, runner="generate", max_model_len=100)
@@ -70,6 +72,7 @@ def _create_proposer(
             max_model_len=model_config.max_model_len,
             is_encoder_decoder=model_config.is_encoder_decoder,
         ),
+        attention_config=AttentionConfig(backend=attention_backend),
     )
 
     return EagleProposer(vllm_config=vllm_config, device=current_platform.device_type)
@@ -331,8 +334,6 @@ def test_load_model(
     use_distinct_lm_head,
     monkeypatch,
 ):
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
-
     if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
         pytest.skip(
             "TRITON_ATTN does not support "
@@ -394,7 +395,9 @@ def test_load_model(
     assert not isinstance(target_model, SupportsMultiModal)
 
     # Create proposer using the helper function
-    proposer = _create_proposer(method, num_speculative_tokens=8)
+    proposer = _create_proposer(
+        method, num_speculative_tokens=8, attention_backend=attn_backend
+    )
 
     # Call the method under test
     proposer.load_model(target_model)
@@ -420,8 +423,6 @@ def test_load_model(
 @pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
 @pytest.mark.parametrize("num_speculative_tokens", [1, 3, 8])
 def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
-    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
-
     if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
         pytest.skip(
             "TRITON_ATTN does not support "
@@ -449,7 +450,9 @@ def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
     seq_lens = [seq_len_1, seq_len_2]
 
     # Create proposer first so we can use its actual hidden_size
-    proposer = _create_proposer("eagle", num_speculative_tokens)
+    proposer = _create_proposer(
+        "eagle", num_speculative_tokens, attention_backend=attn_backend
+    )
     # Get the hidden_size from the proposer to ensure consistency
     hidden_size = proposer.hidden_size
 
@@ -622,7 +625,9 @@ def test_propose_tree(spec_token_tree):
 
     # Create proposer first so we can use its actual hidden_size.
     proposer = _create_proposer(
-        "eagle", num_speculative_tokens, speculative_token_tree=spec_token_tree
+        "eagle",
+        num_speculative_tokens,
+        speculative_token_tree=spec_token_tree,
     )
     # Get the hidden_size from the proposer to ensure consistency.
     hidden_size = proposer.hidden_size

tests/v1/spec_decode/test_max_len.py

@@ -38,9 +38,6 @@ def test_ngram_max_len(num_speculative_tokens: int):
 def test_eagle_max_len(
     monkeypatch: pytest.MonkeyPatch, num_speculative_tokens: int, attn_backend: str
 ):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
-
     if attn_backend == "TRITON_ATTN" and not current_platform.is_rocm():
         pytest.skip(
             "TRITON_ATTN does not support "
@@ -48,7 +45,7 @@ def test_eagle_max_len(
         )
 
     if attn_backend == "ROCM_AITER_FA" and current_platform.is_rocm():
-            m.setenv("VLLM_ROCM_USE_AITER", "1")
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
 
     llm = LLM(
         model="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -60,20 +57,18 @@ def test_eagle_max_len(
             "max_model_len": 80,
         },
         max_model_len=200,
+        attention_config={"backend": attn_backend},
     )
     sampling_params = SamplingParams(max_tokens=200, ignore_eos=True)
     outputs = llm.generate(_PROMPTS, sampling_params)
     for o in outputs:
         assert o.outputs[0].finish_reason == "length", (
-                "This test is only meaningful if the output "
-                "is truncated due to max length"
+            "This test is only meaningful if the output is truncated due to max length"
         )
 
     sampling_params = SamplingParams(
         max_tokens=200,
-            structured_outputs=StructuredOutputsParams(
-                regex="^" + "a b c d e " * 15 + "$"
-            ),
+        structured_outputs=StructuredOutputsParams(regex="^" + "a b c d e " * 15 + "$"),
     )
     output = llm.generate(_PROMPTS, sampling_params)
     for o in output:

vllm/v1/attention/backends/rocm_attn.py

@@ -165,7 +165,7 @@ class RocmAttentionBackend(AttentionBackend):
             raise ValueError(
                 f"Head size {head_size} is not supported by {attn_type}. "
                 f"Supported head sizes are: {cls.get_supported_head_sizes()}. "
-                "Set --attention-config.backend=FLEX_ATTENTION to use "
+                "Set --attention-backend=FLEX_ATTENTION to use "
                 "FlexAttention backend which supports all head sizes."
             )